Import libraries

import pandas as pd
import yfinance as yf
from yahoofinancials import YahooFinancials
import plotly.graph_objects as go
import plotly.express as px
import matplotlib.pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings('ignore')
np.random.seed(42)

def create_line_chart(index_chart,
                      values_chart,
                      end_time):
    
    fig = go.Figure() # create figure
    fig.add_trace(go.Scatter(x=index_chart,
                             y=values_chart ) )

    fig.update_layout(
                    dict(title_text="Portfolio values (from 26/10/2021 - {})".format(end_time),
                         autosize=True,
                        )
                    )
    fig.show()

def create_all_line_chart(watch_list,
                          data,
                          start_time,
                          end_time):

    fig = go.Figure() # create figure
    for i in watch_list:
        fig.add_trace(go.Scatter(x=data.index, y=data[i], name = i) )

    fig.update_layout(
        dict(title_text="STOCK DATA (from {} - {})".format(start_time,end_time),
             autosize=True,
             yaxis_title='Close price')
                        )
    fig.show()

def growth_during_week(data_stock):
    data_sort = pd.DataFrame(100*(data_stock.iloc[-1, :-1]-data_stock.iloc[0, :-1]) / data_stock.iloc[0, :-1]).sort_values([0])
    data_sort['%'] = round(data_sort[0],2).astype(str) + ' %'

    fig = px.bar(data_sort, y = data_sort.index, x = 0, text = data_sort['%'], color=data_sort[0],
                color_continuous_scale='oranges')
    fig.update_layout(
                dict(title_text="Stock rate of return (from {}  to  {})".format(str(data_stock.index[0])[:-9],
                                                                             str(data_stock.index[-1])[:-9]),
                     autosize=True,
                     yaxis_title=None,
                     xaxis_title=None,))
    fig.update_coloraxes(showscale=False)
    fig.show()

def portfolio_df(start_time,
                 end_time,
                 stock_list,
                 count_stock):

    watch_list_df = yf.download(stock_list,
                                start=start_time,
                                end=end_time,
                                progress=False)

    watch_list_df = watch_list_df['Close']
    
    watch_list_df = watch_list_df.reindex(columns=stock_list)
    
    watch_list_df['Portfolio'] = (watch_list_df*count_stock.values()).sum(1)

    return watch_list_df

def description_portfolio(watch_list,
                          data_portfolio,
                          count_stock):
    for i in watch_list:

        print("Price of {} in {} is ----> {}".format(i,
                                                     str(data_portfolio.index[-1])[:10],
                                                     round(data_portfolio[i][-1],3)))
        weight_i = count_stock[i] * data_portfolio[i][-1] / data_portfolio.iloc[-1, -1]
        print("Weight of {} in our portfolio is ----> {}".format(i, round(weight_i,4)))
        print("Overall value of {} in our portfolio is ----> {}".format(i,
              round(data_portfolio[i][-1]*count_stock[i], 3)))
        print("Count of {} stock is ----> {}".format(i, round(count_stock[i],3)))
        print('\n##################################################\n')

def create_line_chart_port(watch_list_port,
                      start_time,
                      end_time):


    watch_df_port = yf.download(watch_list_port,
                       start=start_time,
                       end=end_time,
                       progress=False,
                       )
    watch_df_port = watch_df_port['Close']


    fig = go.Figure() # create figure
    for i in watch_list_port:
        fig.add_trace(go.Scatter(x=watch_df_port.index,
                                 y=watch_df_port[i],
                                 name = i) )

    fig.update_layout(
                dict(title_text="Our stocks data",
                     autosize=True,
                     yaxis_title='Close price')
                    )
    fig.show()

def create_others_price(all_watch_list,
                        portfolio_list,
                        start_time,
                        end_time):
    watch_list_others = [s for s in all_watch_list if s not in portfolio_list]


    watch_df_others = yf.download(watch_list_others,
                           start=start_time,
                           end=end_time,
                           progress=False,
                           )
    watch_df_others  = watch_df_others ['Close']

    create_all_line_chart(watch_list = watch_list_others,
                          data = watch_df_others,
                          start_time = watch_df_others.index[0],
                          end_time = watch_df_others.index[-1])

def price_stock_in_date(stock, date_price):


    watch_df = yf.download(stock,
                           start=date_price,
                           end=date_price,
                           progress=False,
                           )
    watch_df = watch_df['Close']
    return watch_df.values[0]

Load data from Yahoo Finance during 25/10/2018 - 26/10/2020

watch_list = ["AMZN", "C", "AAPL", "IBM", "CSCO",
              "NVDA", "MSFT", "TM", "CVX", "GOOG",
              "FB", "TSLA", "JPM", "JNJ", "BAC",
              "RLLCF", "WMT", "MA", "V", "DIS", "PG"]


watch_df = yf.download(watch_list,
                       start='2018-10-26',
                       end='2021-10-27',
                       progress=False,
                       )['Close']
watch_df

	AAPL	AMZN	BAC	C	CSCO	CVX	DIS	FB	GOOG	IBM	...	JPM	MA	MSFT	NVDA	PG	RLLCF	TM	TSLA	V	WMT
Date
2018-10-25	54.950001	1782.170044	26.590000	64.970001	45.470001	111.580002	114.160004	150.949997	1095.569946	120.889099	...	104.860001	204.199997	108.300003	51.959999	89.000000	NaN	116.660004	62.972000	140.520004	99.180000
2018-10-26	54.075001	1642.810059	26.389999	64.209999	44.250000	111.529999	113.190002	145.369995	1071.469971	119.302101	...	103.419998	197.490005	106.959999	49.572498	87.860001	NaN	117.300003	66.180000	137.740005	98.940002
2018-10-29	53.060001	1538.880005	26.610001	64.349998	43.840000	108.930000	113.040001	142.089996	1020.080017	114.378586	...	104.849998	191.039993	103.849998	46.404999	88.239998	NaN	115.309998	66.970001	134.330002	99.800003
2018-10-30	53.325001	1530.420044	26.780001	64.529999	44.580002	110.849998	114.760002	146.220001	1036.209961	110.325050	...	106.699997	187.729996	103.730003	50.750000	89.190002	NaN	116.239998	65.980003	132.759995	102.419998
2018-10-31	54.715000	1598.010010	27.500000	65.459999	45.750000	111.650002	114.830002	151.789993	1076.770020	110.353729	...	109.019997	197.669998	106.809998	52.707500	88.680000	NaN	117.050003	67.463997	137.850006	100.279999
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-10-20	149.259995	3415.060059	47.090000	71.820000	56.200001	113.160004	170.550003	340.779999	2848.300049	135.659653	...	170.839996	356.769989	307.410004	221.029999	141.210007	0.0210	177.750000	865.799988	231.419998	146.000000
2021-10-21	149.479996	3435.010010	46.830002	70.510002	55.689999	111.739998	171.339996	341.880005	2855.610107	122.686424	...	169.500000	356.209991	310.760010	226.919998	139.330002	0.0198	174.020004	894.000000	230.240005	146.809998
2021-10-22	148.690002	3335.550049	47.570000	71.370003	55.110001	112.800003	169.419998	324.609985	2772.500000	122.256210	...	171.779999	358.670013	309.160004	227.259995	140.830002	0.0188	173.399994	909.679993	231.229996	148.339996
2021-10-25	148.639999	3320.370117	47.509998	70.830002	55.139999	113.849998	172.009995	328.690002	2775.459961	122.026772	...	170.940002	360.859985	308.130005	231.660004	140.850006	0.0188	172.229996	1024.859985	233.880005	150.059998
2021-10-26	149.320007	3376.070068	47.959999	70.650002	55.810001	114.099998	172.039993	315.809998	2793.439941	121.539200	...	171.399994	357.320007	310.109985	247.169998	142.850006	0.0188	173.199997	1018.429993	231.820007	148.750000

756 rows × 21 columns

STOCK DATA (from 2020/10/25 - 2021/10/26)

fig = go.Figure() # create figure
for i in watch_df.columns:
    fig.add_trace(go.Scatter(x=watch_df.index, y=watch_df[i], name = i) )
        
fig.update_layout(
                dict(title_text="STOCK DATA (from 2020/10/25 - 2021/10/26)",
                     autosize=True,
                     yaxis_title='Close price')
                    )
fig.show()

Growth value (from 2018/10/25 - 2021/10/25)

fig = go.Figure()
for i in watch_df.diff().columns:
    fig.add_trace(go.Box(y=watch_df.diff()[i], name=i))


fig.update_layout(
                dict(title_text="Growth value(from 2018/10/25 - 2021/10/25)",
                     autosize=True,
                     yaxis_title='Diff value')
                    )
fig.show()

Growth rate (from 2018/10/25 - 2021/10/26)

fig = go.Figure()
for i in watch_df.diff().columns:
    fig.add_trace(go.Box(y=watch_df.pct_change()[i], name=i))


fig.update_layout(
                dict(title_text="Growth rate (from 2018/10/25 - 2021/10/25)",
                     autosize=True,
                     yaxis_title='Rate of return')
                    )
fig.show()

Based on our analysis we decided to chose these 6 stocks

watch_list_26_10=['FB','GOOG','TSLA',"MSFT","BAC","NVDA"]
watch_list_26_10

['FB', 'GOOG', 'TSLA', 'MSFT', 'BAC', 'NVDA']

fig = go.Figure() # create figure
for i in watch_list_26_10:
    fig.add_trace(go.Scatter(x=watch_df.index, y=watch_df[i], name = i) )
        
fig.update_layout(
                dict(title_text="Our 6 stocks data (from 2020/10/25 - 2021/10/26)",
                     autosize=True,
                     yaxis_title='Close price')
                    )
fig.show()

Histogram of stocks' last 100 rate of returns

fig = go.Figure()

for i in watch_df[watch_list_26_10].columns:
    fig.add_trace(go.Histogram(x=watch_df[watch_list_26_10][-100:].pct_change()[i],name=i,bingroup=5))

# Overlay both histograms
fig.update_layout(barmode='overlay')
# Reduce opacity to see both histograms
fig.update_traces(opacity=0.75)
fig.show()

Histogram of stocks' last 100 diff

fig = go.Figure()

for i in watch_df[watch_list_26_10].columns:
    fig.add_trace(go.Histogram(x=watch_df[watch_list_26_10][-100:].diff()[i],name=i,bingroup=5))

# Overlay both histograms
fig.update_layout(barmode='overlay')
# Reduce opacity to see both histograms
fig.update_traces(opacity=0.75)
fig.show()

watch_df_list=watch_df[watch_list_26_10]
watch_df_list

	FB	GOOG	TSLA	MSFT	BAC	NVDA
Date
2018-10-25	150.949997	1095.569946	62.972000	108.300003	26.590000	51.959999
2018-10-26	145.369995	1071.469971	66.180000	106.959999	26.389999	49.572498
2018-10-29	142.089996	1020.080017	66.970001	103.849998	26.610001	46.404999
2018-10-30	146.220001	1036.209961	65.980003	103.730003	26.780001	50.750000
2018-10-31	151.789993	1076.770020	67.463997	106.809998	27.500000	52.707500
...	...	...	...	...	...	...
2021-10-20	340.779999	2848.300049	865.799988	307.410004	47.090000	221.029999
2021-10-21	341.880005	2855.610107	894.000000	310.760010	46.830002	226.919998
2021-10-22	324.609985	2772.500000	909.679993	309.160004	47.570000	227.259995
2021-10-25	328.690002	2775.459961	1024.859985	308.130005	47.509998	231.660004
2021-10-26	315.809998	2793.439941	1018.429993	310.109985	47.959999	247.169998

756 rows × 6 columns

Our 6 stock rate od returns

watch_df_list_change = watch_df_list.pct_change()
watch_df_list_change

	FB	GOOG	TSLA	MSFT	BAC	NVDA
Date
2018-10-25	NaN	NaN	NaN	NaN	NaN	NaN
2018-10-26	-0.036966	-0.021998	0.050943	-0.012373	-0.007522	-0.045949
2018-10-29	-0.022563	-0.047962	0.011937	-0.029076	0.008337	-0.063896
2018-10-30	0.029066	0.015812	-0.014783	-0.001155	0.006389	0.093632
2018-10-31	0.038093	0.039143	0.022492	0.029692	0.026886	0.038571
...	...	...	...	...	...	...
2021-10-20	0.002324	-0.009783	0.001770	-0.002660	0.013124	-0.008389
2021-10-21	0.003228	0.002566	0.032571	0.010898	-0.005521	0.026648
2021-10-22	-0.050515	-0.029104	0.017539	-0.005149	0.015802	0.001498
2021-10-25	0.012569	0.001068	0.126616	-0.003332	-0.001261	0.019361
2021-10-26	-0.039186	0.006478	-0.006274	0.006426	0.009472	0.066952

756 rows × 6 columns

Generating 15000 portfolios with out 6 stocks

all_portfolios=pd.DataFrame(columns=['portfolio_return',
                                     'portfolio_st_dev',
                                     'weight of '+ watch_df_list.columns[0],
                                     'weight of '+ watch_df_list.columns[1],
                                     'weight of '+ watch_df_list.columns[2],
                                     'weight of '+ watch_df_list.columns[3],
                                     'weight of '+ watch_df_list.columns[4],
                                     'weight of '+ watch_df_list.columns[5]])

number_assets = len(watch_df_list.columns)

number_porfolios = 15000

for porfolios in range(number_porfolios):
    weight = np.random.random(number_assets)
    weight = weight / weight.sum()
    
    porfolio_return=(watch_df_list_change[1:]*weight).sum(1).mean()
    
    portfolio_var=np.dot(np.dot(weight.T,watch_df_list_change.cov()),
                                weight)
    portfolio_st_dev = np.sqrt(portfolio_var)
    
    all_portfolios =all_portfolios.append({'portfolio_return':porfolio_return,
                                           'portfolio_st_dev':portfolio_st_dev,
                                           'weight of '+ watch_df_list.columns[0]:weight[0],
                                          'weight of '+ watch_df_list.columns[1]:weight[1],
                                          'weight of '+ watch_df_list.columns[2]:weight[2],
                                          'weight of '+ watch_df_list.columns[3]:weight[3],
                                          'weight of '+ watch_df_list.columns[4]:weight[4],
                                          'weight of '+ watch_df_list.columns[5]:weight[5]},
                            ignore_index=True) #append row for every car

All possible porfolios with our stocks

fig = go.Figure() # create figure
fig.add_trace(go.Scatter(x=all_portfolios.portfolio_st_dev,
                         y=all_portfolios.portfolio_return,mode='markers') )
        
fig.update_layout(
                dict(title_text="All possible porfolios with our stocks",
                     autosize=True,
                     yaxis_title='Rate of returns',
                     xaxis_title='Standard deviation')
                    )
fig.show()

all_portfolios['Sharpe_ratio']=all_portfolios['portfolio_return']/all_portfolios['portfolio_st_dev']
best_sharpe=all_portfolios.iloc[all_portfolios['Sharpe_ratio'].argmax(),:] # portfolio with maximum Sharpe ratio

best_return = all_portfolios.iloc[all_portfolios['portfolio_return'].argmax(),:] # portfolio with maximum rate of return

best_sd = all_portfolios.iloc[all_portfolios['portfolio_st_dev'].argmin(),:] # porfolio with minimum st dev

Our stocks and best portfolios in all possible portfolios

fig = go.Figure() # create figure
fig.add_trace(go.Scatter(x=all_portfolios.portfolio_st_dev,
                         y=all_portfolios.portfolio_return,mode='markers',showlegend=False) )

fig.add_trace(go.Scatter(x=np.array(best_sharpe[1]),
                         y=np.array(best_sharpe[0]),name='Best from Sharpe',
                         marker=dict(size=[15])) )

fig.add_trace(go.Scatter(x=np.array(best_return[1]),
                         y=np.array(best_return[0]),name='Best from Return',
                        marker=dict(size=[15])) )

fig.add_trace(go.Scatter(x=np.array(best_sd[1]),
                         y=np.array(best_sd[0]),name='Best from SD',
                        marker=dict(size=[15]) ))

for i in watch_df_list_change.columns:
    fig.add_trace(go.Scatter(x=np.array(np.std(watch_df_list_change[i])),
                             y=np.array(np.mean(watch_df_list_change[i])),
                             text=i,
                             name=i,
                             marker=dict(size=[20]) ))
fig.update_traces(textposition="bottom right")      
fig.update_layout(
                dict(title_text="All possible porfolios with our stocks",
                     autosize=True,
                     yaxis_title='Rate of returns',
                     xaxis_title='Standard deviation')
                    )
fig.show()

The weights of our stocks from best Sharpe ratio portfolio

weight_list=watch_df_list[-100:].pct_change().median()/sum(watch_df_list[-100:].pct_change().median())

for i in range(len(best_sharpe[2:-1])):
    weight_list[i]=best_sharpe[2:-1][i]
weight_list

FB      0.003712
GOOG    0.244623
TSLA    0.447205
MSFT    0.049061
BAC     0.001242
NVDA    0.254157
dtype: float64

Portfolio rate of return

watch_df_list_change['porfolio return']=(watch_df_list_change*weight_list.values).sum(1).values
watch_df_list_change

	FB	GOOG	TSLA	MSFT	BAC	NVDA	porfolio return
Date
2018-10-25	NaN	NaN	NaN	NaN	NaN	NaN	0.000000
2018-10-26	-0.036966	-0.021998	0.050943	-0.012373	-0.007522	-0.045949	0.004969
2018-10-29	-0.022563	-0.047962	0.011937	-0.029076	0.008337	-0.063896	-0.024134
2018-10-30	0.029066	0.015812	-0.014783	-0.001155	0.006389	0.093632	0.021114
2018-10-31	0.038093	0.039143	0.022492	0.029692	0.026886	0.038571	0.031068
...	...	...	...	...	...	...	...
2021-10-20	0.002324	-0.009783	0.001770	-0.002660	0.013124	-0.008389	-0.003839
2021-10-21	0.003228	0.002566	0.032571	0.010898	-0.005521	0.026648	0.022506
2021-10-22	-0.050515	-0.029104	0.017539	-0.005149	0.015802	0.001498	0.000684
2021-10-25	0.012569	0.001068	0.126616	-0.003332	-0.001261	0.019361	0.061687
2021-10-26	-0.039186	0.006478	-0.006274	0.006426	0.009472	0.066952	0.015977

756 rows × 7 columns

Historical rate of return of our portfolio

fig = go.Figure() # create figure
fig.add_trace(go.Scatter(x=watch_df_list_change.index[1:],
                         y=watch_df_list_change['porfolio return'] ) )
        
fig.update_layout(
                dict(title_text="Portfolio rate of return (from 2020/10/25 - 2021/10/26)",
                     autosize=True,
                     yaxis_title='rate of return')
                    )
fig.show()

Mean price of our portfolio

watch_df_list['porfolio mean price']=(watch_df_list*weight_list.values).sum(1).values
watch_df_list

	FB	GOOG	TSLA	MSFT	BAC	NVDA	porfolio mean price
Date
2018-10-25	150.949997	1095.569946	62.972000	108.300003	26.590000	51.959999	315.275826
2018-10-26	145.369995	1071.469971	66.180000	106.959999	26.389999	49.572498	310.121544
2018-10-29	142.089996	1020.080017	66.970001	103.849998	26.610001	46.404999	296.934142
2018-10-30	146.220001	1036.209961	65.980003	103.730003	26.780001	50.750000	301.551134
2018-10-31	151.789993	1076.770020	67.463997	106.809998	27.500000	52.707500	312.806903
...	...	...	...	...	...	...	...
2021-10-20	340.779999	2848.300049	865.799988	307.410004	47.090000	221.029999	1156.531870
2021-10-21	341.880005	2855.610107	894.000000	310.760010	46.830002	226.919998	1172.596366
2021-10-22	324.609985	2772.500000	909.679993	309.160004	47.570000	227.259995	1159.222606
2021-10-25	328.690002	2775.459961	1024.859985	308.130005	47.509998	231.660004	1212.538588
2021-10-26	315.809998	2793.439941	1018.429993	310.109985	47.959999	247.169998	1218.053234

756 rows × 7 columns

Historical mean price of our Portfolio

fig = go.Figure() # create figure
fig.add_trace(go.Scatter(x=watch_df_list.index[1:],
                         y=watch_df_list['porfolio mean price'] ) )
        
fig.update_layout(
                dict(title_text="Portfolio mean price (from 2020/10/25 - 2021/10/26)",
                     autosize=True,
                     yaxis_title='Mean price')
                    )
fig.show()

Description of stocks in our portfolio in 26/10/2021

count_stock={}
for i in watch_list_26_10:
    count_stock[i] = 100000*weight_list[i]/watch_df_list[i][-1]

for i in watch_list_26_10:
    print("Price of {} in 26/10/2021 is ----> {}".format(i,round(watch_df_list[i][-1],3)))
    print("Weight of {} in our portfolio is ----> {}".format(i,round(weight_list[i],4)))
    print("Overall value of {} in our portfolio is ----> {}".format(i,round(100000*weight_list[i],3)))
    print("Count of {} stock is ----> {}".format(i,round(count_stock[i],3)))
    print('\n##################################################\n')

Price of FB in 26/10/2021 is ----> 315.81
Weight of FB in our portfolio is ----> 0.0037
Overall value of FB in our portfolio is ----> 371.234
Count of FB stock is ----> 1.175

##################################################

Price of GOOG in 26/10/2021 is ----> 2793.44
Weight of GOOG in our portfolio is ----> 0.2446
Overall value of GOOG in our portfolio is ----> 24462.312
Count of GOOG stock is ----> 8.757

##################################################

Price of TSLA in 26/10/2021 is ----> 1018.43
Weight of TSLA in our portfolio is ----> 0.4472
Overall value of TSLA in our portfolio is ----> 44720.507
Count of TSLA stock is ----> 43.911

##################################################

Price of MSFT in 26/10/2021 is ----> 310.11
Weight of MSFT in our portfolio is ----> 0.0491
Overall value of MSFT in our portfolio is ----> 4906.111
Count of MSFT stock is ----> 15.821

##################################################

Price of BAC in 26/10/2021 is ----> 47.96
Weight of BAC in our portfolio is ----> 0.0012
Overall value of BAC in our portfolio is ----> 124.168
Count of BAC stock is ----> 2.589

##################################################

Price of NVDA in 26/10/2021 is ----> 247.17
Weight of NVDA in our portfolio is ----> 0.2542
Overall value of NVDA in our portfolio is ----> 25415.668
Count of NVDA stock is ----> 102.827

##################################################

Rebalance 03/11/2021

watch_list_26_10

['FB', 'GOOG', 'TSLA', 'MSFT', 'BAC', 'NVDA']

Our 6 stocks prices during 26/10/2021 - 03/11/2021

watch_df_26_10__03_11 = yf.download(watch_list,
                                    start='2021-10-27',
                                    end='2021-11-04',
                                    progress=False,
                                   )
watch_df_26_10__03_11 = watch_df_26_10__03_11['Close']

Our portfolio value during 26/10/2021 - 03/11/2021

watch_list_2=watch_df_26_10__03_11[watch_list_26_10]
watch_list_2['Portfolio']=(watch_list_2*count_stock.values()).sum(1)
watch_list_2['Portfolio'][0]=100000
watch_list_2

	FB	GOOG	TSLA	MSFT	BAC	NVDA	Portfolio
Date
2021-10-26	315.809998	2793.439941	1018.429993	310.109985	47.959999	247.169998	100000.000000
2021-10-27	312.220001	2928.550049	1037.859985	323.170013	47.040001	244.509995	101962.857259
2021-10-28	316.920013	2922.580078	1077.040039	324.350006	47.779999	249.410004	104160.982451
2021-10-29	323.570007	2965.409912	1114.000000	331.619995	47.779999	255.669998	106925.529539
2021-11-01	329.980011	2875.479980	1208.589966	329.369995	47.849998	258.269989	110531.037515
2021-11-02	328.079987	2917.260010	1172.000000	333.130005	47.880001	264.010010	109937.754285
2021-11-03	331.619995	2935.800049	1213.859985	334.000000	48.369999	265.980011	112159.995974

create_line_chart(index_chart = watch_list_2.index,
                  values_chart = watch_list_2['Portfolio'],
                  end_time = '2021/11/03')

create_all_line_chart(watch_list = watch_list_26_10,
                      data = watch_list_2,
                      start_time = '2021/10/26',
                      end_time = '2021/11/03')

Rate of return during 2021/10/26 - 2021/11/03

create_all_line_chart(watch_list = watch_list_26_10,
                      data = watch_list_2.pct_change(),
                      start_time = '2021/10/26',
                      end_time = '2021/11/03')

growth_during_week(data_stock = watch_list_2)

Description of stocks in our portfolio in 03/11/2021

for i in watch_list_26_10:
    print("Price of {} in 03/11/2021 is ----> {}".format(i,
          round(watch_list_2[i][-1], 3)))
    weight_i = count_stock[i] * watch_list_2[i][-1] / watch_list_2.iloc[-1, -1]
    print("Weight of {} in our portfolio is ----> {}".format(i, round(weight_i,4)))
    print("Overall value of {} in our portfolio is ----> {}".format(i,
          round(watch_list_2[i][-1]*count_stock[i], 3)))
    print("Count of {} stock is ----> {}".format(i, round(count_stock[i],3)))
    print('\n##################################################\n')

Price of FB in 03/11/2021 is ----> 331.62
Weight of FB in our portfolio is ----> 0.0035
Overall value of FB in our portfolio is ----> 389.819
Count of FB stock is ----> 1.175

##################################################

Price of GOOG in 03/11/2021 is ----> 2935.8
Weight of GOOG in our portfolio is ----> 0.2292
Overall value of GOOG in our portfolio is ----> 25708.967
Count of GOOG stock is ----> 8.757

##################################################

Price of TSLA in 03/11/2021 is ----> 1213.86
Weight of TSLA in our portfolio is ----> 0.4752
Overall value of TSLA in our portfolio is ----> 53302.077
Count of TSLA stock is ----> 43.911

##################################################

Price of MSFT in 03/11/2021 is ----> 334.0
Weight of MSFT in our portfolio is ----> 0.0471
Overall value of MSFT in our portfolio is ----> 5284.065
Count of MSFT stock is ----> 15.821

##################################################

Price of BAC in 03/11/2021 is ----> 48.37
Weight of BAC in our portfolio is ----> 0.0011
Overall value of BAC in our portfolio is ----> 125.229
Count of BAC stock is ----> 2.589

##################################################

Price of NVDA in 03/11/2021 is ----> 265.98
Weight of NVDA in our portfolio is ----> 0.2438
Overall value of NVDA in our portfolio is ----> 27349.839
Count of NVDA stock is ----> 102.827

##################################################

Dynamics of other stosk during 26/10/2021 - 03/11/2021

watch_list_others = ["AMZN", "C", "AAPL", "IBM", "CSCO",
              "TM", "CVX", "JPM", "JNJ",
              "RLLCF", "WMT", "MA", "V", "DIS", "PG"]


watch_df_others = yf.download(watch_list_others,
                       start='2021-10-27',
                       end='2021-11-04',
                       progress=False,
                       )
watch_df_others  = watch_df_others ['Close']

fig = go.Figure() # create figure
for i in watch_list_others:
    fig.add_trace(go.Scatter(x=watch_df_others.index, y=watch_df_others.pct_change()[i], name = i) )
        
fig.update_layout(
    dict(title_text="Rate of returns (from 2021/10/26 - 2021/11/03)",
         autosize=True,
         yaxis_title='rate of return')
                    )
fig.show()

fig = go.Figure() # create figure
for i in watch_list_others:
    fig.add_trace(go.Scatter(x=watch_df_others.index, y=watch_df_others[i], name = i) )
        
fig.update_layout(
    dict(title_text="STOCK DATA (from 2021/10/26 - 2021/11/03)",
         autosize=True,
         yaxis_title='Close price')
                    )
fig.show()

Rate of return of other stocks for 1 week

100*(watch_df_others.iloc[-1, :]-watch_df_others.iloc[0, :]) / watch_df_others.iloc[0, :]

AAPL      1.453253
AMZN      0.234886
C        -2.335459
CSCO      3.296900
CVX      -0.955299
DIS      -1.139265
IBM       0.000000
JNJ      -0.416291
JPM      -0.507582
MA       -7.805332
PG        1.540075
RLLCF    -9.574462
TM        4.543886
V       -10.232077
WMT       0.961340
dtype: float64

Our new rebalanced portfolio in 03/11/2011

new_03_11_2021_list = ['FB', 'GOOG', 'TSLA', 'MSFT','NVDA', 'TM' ]

watch_df_03_11 = yf.download(new_03_11_2021_list,
                                    start='2021-11-04',
                                    end='2021-11-04',
                                    progress=False,
                                   )
watch_df_03_11 = watch_df_03_11['Close']

watch_df_03_11

	FB	GOOG	MSFT	NVDA	TM	TSLA
Date
2021-11-03	331.619995	2935.800049	334.0	265.980011	181.070007	1213.859985

count_TM = (watch_list_2.iloc[-1, -1]*count_stock['BAC'] * watch_list_2['BAC'][-1] / watch_list_2.iloc[-1, -1]) / watch_df_others['TM'][-1]
count_stock["TM"] = count_stock.pop("BAC")
count_stock['TM'] = count_TM

for i in new_03_11_2021_list:
    print("Price of {} in 03/11/2021 is ----> {}".format(i,
          round(watch_df_03_11[i][-1], 3)))
    weight_i = count_stock[i] * watch_df_03_11[i][-1] / watch_list_2.iloc[-1, -1]
    print("Weight of {} in our portfolio is ----> {}".format(i, round(weight_i,4)))
    print("Overall value of {} in our portfolio is ----> {}".format(i,
          round(watch_df_03_11[i][-1]*count_stock[i], 3)))
    print("Count of {} stock is ----> {}".format(i, round(count_stock[i],3)))
    print('\n##################################################\n')

Price of FB in 03/11/2021 is ----> 331.62
Weight of FB in our portfolio is ----> 0.0035
Overall value of FB in our portfolio is ----> 389.819
Count of FB stock is ----> 1.175

##################################################

Price of GOOG in 03/11/2021 is ----> 2935.8
Weight of GOOG in our portfolio is ----> 0.2292
Overall value of GOOG in our portfolio is ----> 25708.967
Count of GOOG stock is ----> 8.757

##################################################

Price of TSLA in 03/11/2021 is ----> 1213.86
Weight of TSLA in our portfolio is ----> 0.4752
Overall value of TSLA in our portfolio is ----> 53302.077
Count of TSLA stock is ----> 43.911

##################################################

Price of MSFT in 03/11/2021 is ----> 334.0
Weight of MSFT in our portfolio is ----> 0.0471
Overall value of MSFT in our portfolio is ----> 5284.065
Count of MSFT stock is ----> 15.821

##################################################

Price of NVDA in 03/11/2021 is ----> 265.98
Weight of NVDA in our portfolio is ----> 0.2438
Overall value of NVDA in our portfolio is ----> 27349.839
Count of NVDA stock is ----> 102.827

##################################################

Price of TM in 03/11/2021 is ----> 181.07
Weight of TM in our portfolio is ----> 0.0011
Overall value of TM in our portfolio is ----> 125.229
Count of TM stock is ----> 0.692

##################################################

Rebalance 10/11/2021

Stock prices during 03/11/2021 - 10/11/2021

data_03_10=portfolio_df(start_time='2021-11-04',
                         end_time='2021-11-11',
                         stock_list=['FB', 'GOOG', 'TSLA', 'MSFT', 'NVDA', 'TM'],
                         count_stock=count_stock)

data_03_10

	FB	GOOG	TSLA	MSFT	NVDA	TM	Portfolio
Date
2021-11-03	331.619995	2935.800049	1213.859985	334.000000	265.980011	181.070007	112159.995974
2021-11-04	335.850006	2973.659912	1229.910034	336.440002	298.010010	181.479996	116533.710482
2021-11-05	341.130005	2984.820068	1222.089966	336.059998	297.519989	179.279999	116236.337841
2021-11-08	338.619995	2987.030029	1162.939941	336.989990	308.040009	180.479996	114752.671645
2021-11-09	335.369995	2984.969971	1023.500000	335.950012	306.570007	176.320007	108437.347347
2021-11-10	327.640015	2932.520020	1067.949951	330.799988	294.589996	177.360001	108608.183705

portfolio_values = watch_list_2['Portfolio']
portfolio_values=portfolio_values.append(data_03_10['Portfolio'][1:])

Portfolio values during 26/10/2021 to 11/11/2021

create_line_chart(index_chart = portfolio_values.index,
                  values_chart = portfolio_values.values,
                  end_time = '11/11/2021')

create_all_line_chart(watch_list = ['FB', 'GOOG', 'TSLA', 'MSFT', 'NVDA', 'TM'],
                      data = data_03_10,
                      start_time = '03/11/2021',
                      end_time = '10/11/2021')

Rate of return of stocks

create_all_line_chart(watch_list = ['FB', 'GOOG', 'TSLA', 'MSFT', 'NVDA', 'TM'],
                      data = data_03_10.pct_change(),
                      start_time = '03/11/2021',
                      end_time = '10/11/2021')

Rate of return of our stocks

growth_during_week(data_stock = data_03_10)

Description of stocks in our portfolio in 10/11/2021

description_portfolio(watch_list = ['FB', 'GOOG', 'TSLA', 'MSFT', 'NVDA', 'TM'],
                      data_portfolio = data_03_10,
                      count_stock = count_stock)

Price of FB in 2021-11-10 is ----> 327.64
Weight of FB in our portfolio is ----> 0.0035
Overall value of FB in our portfolio is ----> 385.14
Count of FB stock is ----> 1.175

##################################################

Price of GOOG in 2021-11-10 is ----> 2932.52
Weight of GOOG in our portfolio is ----> 0.2364
Overall value of GOOG in our portfolio is ----> 25680.244
Count of GOOG stock is ----> 8.757

##################################################

Price of TSLA in 2021-11-10 is ----> 1067.95
Weight of TSLA in our portfolio is ----> 0.4318
Overall value of TSLA in our portfolio is ----> 46894.989
Count of TSLA stock is ----> 43.911

##################################################

Price of MSFT in 2021-11-10 is ----> 330.8
Weight of MSFT in our portfolio is ----> 0.0482
Overall value of MSFT in our portfolio is ----> 5233.439
Count of MSFT stock is ----> 15.821

##################################################

Price of NVDA in 2021-11-10 is ----> 294.59
Weight of NVDA in our portfolio is ----> 0.2789
Overall value of NVDA in our portfolio is ----> 30291.709
Count of NVDA stock is ----> 102.827

##################################################

Price of TM in 2021-11-10 is ----> 177.36
Weight of TM in our portfolio is ----> 0.0011
Overall value of TM in our portfolio is ----> 122.663
Count of TM stock is ----> 0.692

##################################################

Prices of other stocks

watch_list_others = ["AMZN", "C", "AAPL", "IBM", "CSCO",
              "BAC", "CVX", "JPM", "JNJ",
              "RLLCF", "WMT", "MA", "V", "DIS", "PG"]


watch_df_others = yf.download(watch_list_others,
                       start='2021-11-04',
                       end='2021-11-11',
                       progress=False,
                       )
watch_df_others  = watch_df_others ['Close']

create_all_line_chart(watch_list = watch_list_others,
                      data = watch_df_others,
                      start_time = '03/11/2021',
                      end_time = '10/11/2021')

Change structures of our portfolio

Selling $30000 from TSLA

count_stock['TSLA']-= (30000/data_03_10['TSLA'][-1])

Buying $15000 NVDA

count_stock['NVDA'] += (15000/data_03_10['NVDA'][-1])

count_stock

{'FB': 1.1754982536031042,
 'GOOG': 8.757056585932718,
 'TSLA': 15.820019554826416,
 'MSFT': 15.820552884763933,
 'NVDA': 153.74489711577996,
 'TM': 0.6916057658915029}

df_11_11 = yf.download(['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN','MA'],
            start='2021-11-11',
            end='2021-11-11',
            progress=False)

df_11_11 = df_11_11['Close']
    
df_11_11 = df_11_11.reindex(columns=['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN','MA'])

df_11_11

	FB	GOOG	TSLA	MSFT	NVDA	TM	AMZN	MA
Date
2021-11-10	327.640015	2932.52002	1067.949951	330.799988	294.589996	177.360001	3482.050049	357.940002

Buying $10000 AMZN

Buying $5000 MA

count_stock['AMZN'] = (10000/df_11_11['AMZN'][-1])
count_stock['MA'] = (5000/df_11_11['MA'][-1])

count_stock

{'FB': 1.1754982536031042,
 'GOOG': 8.757056585932718,
 'TSLA': 15.820019554826416,
 'MSFT': 15.820552884763933,
 'NVDA': 153.74489711577996,
 'TM': 0.6916057658915029,
 'AMZN': 2.871871414761966,
 'MA': 13.968821494933318}

Our portfolio in 10/11/2021

df_11_11= portfolio_df(start_time='2021-11-11',
                         end_time='2021-11-11',
                         stock_list=['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN','MA'],
                       count_stock=count_stock)

df_11_11

	FB	GOOG	TSLA	MSFT	NVDA	TM	AMZN	MA	Portfolio
Date
2021-11-10	327.640015	2932.52002	1067.949951	330.799988	294.589996	177.360001	3482.050049	357.940002	108608.183705

Final description of stocks in our portfolio in 10/11/2021

description_portfolio(watch_list = ['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN','MA'],
                      data_portfolio = df_11_11,
                      count_stock = count_stock)

Price of FB in 2021-11-10 is ----> 327.64
Weight of FB in our portfolio is ----> 0.0035
Overall value of FB in our portfolio is ----> 385.14
Count of FB stock is ----> 1.175

##################################################

Price of GOOG in 2021-11-10 is ----> 2932.52
Weight of GOOG in our portfolio is ----> 0.2364
Overall value of GOOG in our portfolio is ----> 25680.244
Count of GOOG stock is ----> 8.757

##################################################

Price of TSLA in 2021-11-10 is ----> 1067.95
Weight of TSLA in our portfolio is ----> 0.1556
Overall value of TSLA in our portfolio is ----> 16894.989
Count of TSLA stock is ----> 15.82

##################################################

Price of MSFT in 2021-11-10 is ----> 330.8
Weight of MSFT in our portfolio is ----> 0.0482
Overall value of MSFT in our portfolio is ----> 5233.439
Count of MSFT stock is ----> 15.821

##################################################

Price of NVDA in 2021-11-10 is ----> 294.59
Weight of NVDA in our portfolio is ----> 0.417
Overall value of NVDA in our portfolio is ----> 45291.709
Count of NVDA stock is ----> 153.745

##################################################

Price of TM in 2021-11-10 is ----> 177.36
Weight of TM in our portfolio is ----> 0.0011
Overall value of TM in our portfolio is ----> 122.663
Count of TM stock is ----> 0.692

##################################################

Price of AMZN in 2021-11-10 is ----> 3482.05
Weight of AMZN in our portfolio is ----> 0.0921
Overall value of AMZN in our portfolio is ----> 10000.0
Count of AMZN stock is ----> 2.872

##################################################

Price of MA in 2021-11-10 is ----> 357.94
Weight of MA in our portfolio is ----> 0.046
Overall value of MA in our portfolio is ----> 5000.0
Count of MA stock is ----> 13.969

##################################################

Rebalance 17/11/2021

Stock prices during 10/11/2021 - 1711/2021

data_10_17=portfolio_df(start_time='2021-11-11',
                         end_time='2021-11-18',
                         stock_list=['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN','MA'],
                         count_stock=count_stock)

data_10_17

	FB	GOOG	TSLA	MSFT	NVDA	TM	AMZN	MA	Portfolio
Date
2021-11-10	327.640015	2932.520020	1067.949951	330.799988	294.589996	177.360001	3482.050049	357.940002	108608.183705
2021-11-11	327.739990	2934.959961	1063.510010	332.429993	303.899994	179.029999	3472.500000	354.339996	109940.020792
2021-11-12	340.890015	2992.909912	1033.420044	336.720001	303.899994	184.399994	3525.149902	363.500000	110337.668209
2021-11-15	347.559998	2987.760010	1013.390015	336.070007	300.250000	184.389999	3545.679932	361.359985	109441.143331
2021-11-16	342.959991	2981.520020	1054.729980	339.510010	302.029999	187.429993	3540.699951	369.559998	110465.524742
2021-11-17	340.769989	2981.239990	1089.010010	339.119995	292.609985	186.210007	3549.000000	359.170013	109426.216747

portfolio_values=portfolio_values.append(data_10_17['Portfolio'][1:])

Portfolio values during 10/11/2021 - 17/11/2021

create_line_chart(index_chart = portfolio_values.index,
                  values_chart = portfolio_values.values,
                  end_time = '11/17/2021')

Our stok proces

create_all_line_chart(watch_list = ['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN','MA'],
                      data = data_10_17,
                      start_time = '10/11/2021',
                      end_time = '17/11/2021')

Our stock rate of return

create_all_line_chart(watch_list = ['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN','MA'],
                      data = data_10_17.pct_change(),
                      start_time = '10/11/2021',
                      end_time = '17/11/2021')

Our portfolio rate of return during last week

growth_during_week(data_stock = data_10_17)

Portfolio description

description_portfolio(watch_list = ['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN','MA'],
                      data_portfolio = data_10_17,
                      count_stock = count_stock)

Price of FB in 2021-11-17 is ----> 340.77
Weight of FB in our portfolio is ----> 0.0037
Overall value of FB in our portfolio is ----> 400.575
Count of FB stock is ----> 1.175

##################################################

Price of GOOG in 2021-11-17 is ----> 2981.24
Weight of GOOG in our portfolio is ----> 0.2386
Overall value of GOOG in our portfolio is ----> 26106.887
Count of GOOG stock is ----> 8.757

##################################################

Price of TSLA in 2021-11-17 is ----> 1089.01
Weight of TSLA in our portfolio is ----> 0.1574
Overall value of TSLA in our portfolio is ----> 17228.16
Count of TSLA stock is ----> 15.82

##################################################

Price of MSFT in 2021-11-17 is ----> 339.12
Weight of MSFT in our portfolio is ----> 0.049
Overall value of MSFT in our portfolio is ----> 5365.066
Count of MSFT stock is ----> 15.821

##################################################

Price of NVDA in 2021-11-17 is ----> 292.61
Weight of NVDA in our portfolio is ----> 0.4111
Overall value of NVDA in our portfolio is ----> 44987.292
Count of NVDA stock is ----> 153.745

##################################################

Price of TM in 2021-11-17 is ----> 186.21
Weight of TM in our portfolio is ----> 0.0012
Overall value of TM in our portfolio is ----> 128.784
Count of TM stock is ----> 0.692

##################################################

Price of AMZN in 2021-11-17 is ----> 3549.0
Weight of AMZN in our portfolio is ----> 0.0931
Overall value of AMZN in our portfolio is ----> 10192.272
Count of AMZN stock is ----> 2.872

##################################################

Price of MA in 2021-11-17 is ----> 359.17
Weight of MA in our portfolio is ----> 0.0458
Overall value of MA in our portfolio is ----> 5017.182
Count of MA stock is ----> 13.969

##################################################

Other stok prices

create_others_price(all_watch_list = watch_list,
                    portfolio_list = ['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN','MA'],
                    start_time = '2021-11-11',
                    end_time = '2021-11-17')

We achieved some results from GARCH model, but this is not good enough

from arch import arch_model
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from datetime import datetime, timedelta
returns = watch_df_others = yf.download(watch_list,
                                           start='2021-01-01',
                                           end='2021-11-17',
                                           progress=False,
                                           )['Close'].pct_change()
mean_forecast ={}
for i in range(len(returns.columns)):
    train = returns.iloc[1:,i]
    model = arch_model(train, p=3, q=3)
    model_fit = model.fit(disp='off')
    pred = model_fit.forecast(horizon=7)
    future_dates = [returns.index[-1] + timedelta(days=i) for i in range(1,8)]
    pred = pd.Series(np.sqrt(pred.variance.values[-1,:]), index=future_dates)
    mean_forecast[returns.columns[i]] = round(pred.max(),3)

mean_forecast = dict( sorted(mean_forecast.items(),
                             key=lambda item: item[1],
                             reverse=True))
mean_forecast

{'RLLCF': 0.618,
 'TSLA': 0.05,
 'NVDA': 0.035,
 'DIS': 0.024,
 'FB': 0.019,
 'TM': 0.019,
 'V': 0.019,
 'MA': 0.018,
 'BAC': 0.016,
 'CVX': 0.016,
 'AMZN': 0.015,
 'C': 0.015,
 'IBM': 0.015,
 'GOOG': 0.014,
 'JPM': 0.013,
 'AAPL': 0.012,
 'MSFT': 0.012,
 'CSCO': 0.011,
 'JNJ': 0.009,
 'WMT': 0.009,
 'PG': 0.008}

Sell all MA stock

# overall MA value
MA_value = count_stock['MA'] * data_10_17['MA'][-1]

del count_stock['MA']

By that money we bought TSLA

count_stock['TSLA'] += MA_value / data_10_17['TSLA'][-1]

Our stockscounts

count_stock

{'FB': 1.1754982536031042,
 'GOOG': 8.757056585932718,
 'TSLA': 20.427123033135686,
 'MSFT': 15.820552884763933,
 'NVDA': 153.74489711577996,
 'TM': 0.6916057658915029,
 'AMZN': 2.871871414761966}

Our portfolio in 17/11/2021

df_17_17= portfolio_df(start_time='2021-11-18',
                         end_time='2021-11-18',
                         stock_list=['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN'],
                       count_stock=count_stock)

df_17_17

	FB	GOOG	TSLA	MSFT	NVDA	TM	AMZN	Portfolio
Date
2021-11-17	340.769989	2981.23999	1089.01001	339.119995	292.609985	186.210007	3549.0	109426.216747

Description of our portfolio in 17/11/2021 after rebalance

description_portfolio(watch_list = ['FB','GOOG','TSLA','MSFT','NVDA','TM','AMZN'],
                      data_portfolio = df_17_17,
                      count_stock = count_stock)

Price of FB in 2021-11-17 is ----> 340.77
Weight of FB in our portfolio is ----> 0.0037
Overall value of FB in our portfolio is ----> 400.575
Count of FB stock is ----> 1.175

##################################################

Price of GOOG in 2021-11-17 is ----> 2981.24
Weight of GOOG in our portfolio is ----> 0.2386
Overall value of GOOG in our portfolio is ----> 26106.887
Count of GOOG stock is ----> 8.757

##################################################

Price of TSLA in 2021-11-17 is ----> 1089.01
Weight of TSLA in our portfolio is ----> 0.2033
Overall value of TSLA in our portfolio is ----> 22245.341
Count of TSLA stock is ----> 20.427

##################################################

Price of MSFT in 2021-11-17 is ----> 339.12
Weight of MSFT in our portfolio is ----> 0.049
Overall value of MSFT in our portfolio is ----> 5365.066
Count of MSFT stock is ----> 15.821

##################################################

Price of NVDA in 2021-11-17 is ----> 292.61
Weight of NVDA in our portfolio is ----> 0.4111
Overall value of NVDA in our portfolio is ----> 44987.292
Count of NVDA stock is ----> 153.745

##################################################

Price of TM in 2021-11-17 is ----> 186.21
Weight of TM in our portfolio is ----> 0.0012
Overall value of TM in our portfolio is ----> 128.784
Count of TM stock is ----> 0.692

##################################################

Price of AMZN in 2021-11-17 is ----> 3549.0
Weight of AMZN in our portfolio is ----> 0.0931
Overall value of AMZN in our portfolio is ----> 10192.272
Count of AMZN stock is ----> 2.872

##################################################

Rebalance 24/11/2021

Stock prices during 10/11/2021 - 17/11/2021

data_17_24 = portfolio_df(start_time='2021-11-18',
                          end_time='2021-11-25',
                          stock_list=['FB', 'GOOG', 'TSLA','MSFT', 'NVDA', 'TM', 'AMZN'],
                          count_stock=count_stock)


data_17_24

	FB	GOOG	TSLA	MSFT	NVDA	TM	AMZN	Portfolio
Date
2021-11-17	340.769989	2981.239990	1089.010010	339.119995	292.609985	186.210007	3549.000000	109426.216747
2021-11-18	338.690002	3014.179932	1096.380005	341.269989	316.750000	184.649994	3696.060059	114029.453279
2021-11-19	345.299988	2999.050049	1137.060059	343.109985	329.850006	185.960007	3676.570068	116723.808644
2021-11-22	341.010010	2941.570068	1156.869995	339.829987	319.559998	185.089996	3572.570068	114686.866321
2021-11-23	337.250000	2935.139893	1109.030029	337.679993	317.459991	185.059998	3580.040039	113313.456889
2021-11-24	341.059998	2934.350098	1116.000000	337.910004	326.739990	184.809998	3580.409912	114884.676390

portfolio_values=portfolio_values.append(data_17_24['Portfolio'][1:])

Portfolio values during all time

create_line_chart(index_chart = portfolio_values.index,
                  values_chart = portfolio_values.values,
                  end_time = '24/11/2021')

Portdolio description before rebalance in 24/11/2021

description_portfolio(watch_list = ['FB', 'GOOG', 'TSLA','MSFT', 'NVDA', 'TM', 'AMZN'],
                      data_portfolio = data_17_24,
                      count_stock = count_stock)

Price of FB in 2021-11-24 is ----> 341.06
Weight of FB in our portfolio is ----> 0.0035
Overall value of FB in our portfolio is ----> 400.915
Count of FB stock is ----> 1.175

##################################################

Price of GOOG in 2021-11-24 is ----> 2934.35
Weight of GOOG in our portfolio is ----> 0.2237
Overall value of GOOG in our portfolio is ----> 25696.27
Count of GOOG stock is ----> 8.757

##################################################

Price of TSLA in 2021-11-24 is ----> 1116.0
Weight of TSLA in our portfolio is ----> 0.1984
Overall value of TSLA in our portfolio is ----> 22796.669
Count of TSLA stock is ----> 20.427

##################################################

Price of MSFT in 2021-11-24 is ----> 337.91
Weight of MSFT in our portfolio is ----> 0.0465
Overall value of MSFT in our portfolio is ----> 5345.923
Count of MSFT stock is ----> 15.821

##################################################

Price of NVDA in 2021-11-24 is ----> 326.74
Weight of NVDA in our portfolio is ----> 0.4373
Overall value of NVDA in our portfolio is ----> 50234.606
Count of NVDA stock is ----> 153.745

##################################################

Price of TM in 2021-11-24 is ----> 184.81
Weight of TM in our portfolio is ----> 0.0011
Overall value of TM in our portfolio is ----> 127.816
Count of TM stock is ----> 0.692

##################################################

Price of AMZN in 2021-11-24 is ----> 3580.41
Weight of AMZN in our portfolio is ----> 0.0895
Overall value of AMZN in our portfolio is ----> 10282.477
Count of AMZN stock is ----> 2.872

##################################################

Our stok proces

create_all_line_chart(watch_list = ['FB', 'GOOG', 'TSLA','MSFT', 'NVDA', 'TM', 'AMZN'],
                      data = data_17_24,
                      start_time = '17/11/2021',
                      end_time = '24/11/2021')

Our stock rate of return

create_all_line_chart(watch_list = ['FB', 'GOOG', 'TSLA','MSFT', 'NVDA', 'TM', 'AMZN'],
                      data = data_17_24.pct_change(),
                      start_time = '17/11/2021',
                      end_time = '24/11/2021')

Our portfolio rate of return during last week

growth_during_week(data_stock = data_17_24)

Other stok prices

create_others_price(all_watch_list = watch_list,
                    portfolio_list = ['FB', 'GOOG', 'TSLA','MSFT', 'NVDA', 'TM', 'AMZN'],
                    start_time = '2021-10-01',
                    end_time = '2021-11-25')

Sell all GOOG stock

# overall GOOG value
GOOG_value = count_stock['GOOG'] * data_17_24['GOOG'][-1]
del count_stock['GOOG']

Sell all AMZN stock

# overall AMZN value
AMZN_value = count_stock['AMZN'] * data_17_24['AMZN'][-1]
del count_stock['AMZN']

Buy AAPL stock with $12000

Buy WMT stock with $6000

Buy CSCO stock with $6000

Buy V stock with $6000

Buy PG stock with $5979

count_stock['AAPL'] = (12000 / price_stock_in_date('AAPL','2021-11-25'))
count_stock['WMT'] = (6000 / price_stock_in_date('WMT','2021-11-25'))
count_stock['CSCO'] = (6000 / price_stock_in_date('CSCO','2021-11-25'))
count_stock['V'] = (6000 / price_stock_in_date('V','2021-11-25'))
count_stock['PG'] = ((GOOG_value + AMZN_value - 30000) / price_stock_in_date('PG','2021-11-25'))

data_25_11=portfolio_df(start_time='2021-11-25',
                         end_time='2021-11-25',
                         stock_list=['FB', 'TSLA','MSFT', 'NVDA', 'TM', 'AAPL','WMT','CSCO','V','PG'],
                         count_stock=count_stock)

data_25_11

	FB	TSLA	MSFT	NVDA	TM	AAPL	WMT	CSCO	V	PG	Portfolio
Date
2021-11-24	341.059998	1116.0	337.910004	326.73999	184.809998	161.940002	146.539993	55.540001	203.25	148.660004	114884.67639

Portfolio description in 24/11/2021

description_portfolio(watch_list = ['FB', 'TSLA','MSFT', 'NVDA', 'TM', 'AAPL','WMT','CSCO','V','PG'],
                      data_portfolio = data_25_11,
                      count_stock = count_stock)

Price of FB in 2021-11-24 is ----> 341.06
Weight of FB in our portfolio is ----> 0.0035
Overall value of FB in our portfolio is ----> 400.915
Count of FB stock is ----> 1.175

##################################################

Price of TSLA in 2021-11-24 is ----> 1116.0
Weight of TSLA in our portfolio is ----> 0.1984
Overall value of TSLA in our portfolio is ----> 22796.669
Count of TSLA stock is ----> 20.427

##################################################

Price of MSFT in 2021-11-24 is ----> 337.91
Weight of MSFT in our portfolio is ----> 0.0465
Overall value of MSFT in our portfolio is ----> 5345.923
Count of MSFT stock is ----> 15.821

##################################################

Price of NVDA in 2021-11-24 is ----> 326.74
Weight of NVDA in our portfolio is ----> 0.4373
Overall value of NVDA in our portfolio is ----> 50234.606
Count of NVDA stock is ----> 153.745

##################################################

Price of TM in 2021-11-24 is ----> 184.81
Weight of TM in our portfolio is ----> 0.0011
Overall value of TM in our portfolio is ----> 127.816
Count of TM stock is ----> 0.692

##################################################

Price of AAPL in 2021-11-24 is ----> 161.94
Weight of AAPL in our portfolio is ----> 0.1045
Overall value of AAPL in our portfolio is ----> 12000.0
Count of AAPL stock is ----> 74.102

##################################################

Price of WMT in 2021-11-24 is ----> 146.54
Weight of WMT in our portfolio is ----> 0.0522
Overall value of WMT in our portfolio is ----> 6000.0
Count of WMT stock is ----> 40.944

##################################################

Price of CSCO in 2021-11-24 is ----> 55.54
Weight of CSCO in our portfolio is ----> 0.0522
Overall value of CSCO in our portfolio is ----> 6000.0
Count of CSCO stock is ----> 108.03

##################################################

Price of V in 2021-11-24 is ----> 203.25
Weight of V in our portfolio is ----> 0.0522
Overall value of V in our portfolio is ----> 6000.0
Count of V stock is ----> 29.52

##################################################

Price of PG in 2021-11-24 is ----> 148.66
Weight of PG in our portfolio is ----> 0.052
Overall value of PG in our portfolio is ----> 5978.747
Count of PG stock is ----> 40.218

##################################################

Rebalance 01/12/2021

data_24_01 = portfolio_df(start_time='2021-11-25',
                          end_time='2021-12-02',
                          stock_list=['FB', 'TSLA', 'MSFT', 'NVDA',
                                      'TM', 'AAPL', 'WMT', 'CSCO', 'V', 'PG'],
                          count_stock=count_stock)


data_24_01

	FB	TSLA	MSFT	NVDA	TM	AAPL	WMT	CSCO	V	PG	Portfolio
Date
2021-11-24	341.059998	1116.000000	337.910004	326.739990	184.809998	161.940002	146.539993	55.540001	203.250000	148.660004	114884.676390
2021-11-26	333.119995	1081.920044	329.679993	315.029999	179.830002	156.809998	144.899994	54.669998	197.649994	147.470001	111490.739026
2021-11-29	338.029999	1136.989990	336.630005	333.760010	177.470001	160.240005	142.630005	55.759998	196.289993	149.020004	115910.563836
2021-11-30	324.459991	1144.760010	330.589996	326.760010	177.850006	165.300003	140.630005	54.840000	193.770004	144.580002	114822.543676
2021-12-01	310.600006	1095.000000	330.079987	314.350006	178.289993	164.770004	137.139999	55.029999	190.160004	146.399994	111679.041293

portfolio_values=portfolio_values.append(data_24_01['Portfolio'][1:])

Portfolio values during all time

create_line_chart(index_chart = portfolio_values.index,
                  values_chart = portfolio_values.values,
                  end_time = '01/12/2021')

Portdolio description before rebalance in 01/12/2021

description_portfolio(watch_list = ['FB', 'TSLA', 'MSFT', 'NVDA',
                                    'TM', 'AAPL', 'WMT', 'CSCO', 'V', 'PG'],
                      data_portfolio = data_24_01,
                      count_stock = count_stock)

Price of FB in 2021-12-01 is ----> 310.6
Weight of FB in our portfolio is ----> 0.0033
Overall value of FB in our portfolio is ----> 365.11
Count of FB stock is ----> 1.175

##################################################

Price of TSLA in 2021-12-01 is ----> 1095.0
Weight of TSLA in our portfolio is ----> 0.2003
Overall value of TSLA in our portfolio is ----> 22367.7
Count of TSLA stock is ----> 20.427

##################################################

Price of MSFT in 2021-12-01 is ----> 330.08
Weight of MSFT in our portfolio is ----> 0.0468
Overall value of MSFT in our portfolio is ----> 5222.048
Count of MSFT stock is ----> 15.821

##################################################

Price of NVDA in 2021-12-01 is ----> 314.35
Weight of NVDA in our portfolio is ----> 0.4328
Overall value of NVDA in our portfolio is ----> 48329.709
Count of NVDA stock is ----> 153.745

##################################################

Price of TM in 2021-12-01 is ----> 178.29
Weight of TM in our portfolio is ----> 0.0011
Overall value of TM in our portfolio is ----> 123.306
Count of TM stock is ----> 0.692

##################################################

Price of AAPL in 2021-12-01 is ----> 164.77
Weight of AAPL in our portfolio is ----> 0.1093
Overall value of AAPL in our portfolio is ----> 12209.707
Count of AAPL stock is ----> 74.102

##################################################

Price of WMT in 2021-12-01 is ----> 137.14
Weight of WMT in our portfolio is ----> 0.0503
Overall value of WMT in our portfolio is ----> 5615.122
Count of WMT stock is ----> 40.944

##################################################

Price of CSCO in 2021-12-01 is ----> 55.03
Weight of CSCO in our portfolio is ----> 0.0532
Overall value of CSCO in our portfolio is ----> 5944.904
Count of CSCO stock is ----> 108.03

##################################################

Price of V in 2021-12-01 is ----> 190.16
Weight of V in our portfolio is ----> 0.0503
Overall value of V in our portfolio is ----> 5613.579
Count of V stock is ----> 29.52

##################################################

Price of PG in 2021-12-01 is ----> 146.4
Weight of PG in our portfolio is ----> 0.0527
Overall value of PG in our portfolio is ----> 5887.855
Count of PG stock is ----> 40.218

##################################################

Our stok proces

create_all_line_chart(watch_list = ['FB', 'TSLA', 'MSFT', 'NVDA',
                                    'TM', 'AAPL', 'WMT', 'CSCO', 'V', 'PG'],
                      data = data_24_01,
                      start_time = '24/11/2021',
                      end_time = '01/12/2021')

Our stock rate of return

create_all_line_chart(watch_list = ['FB', 'TSLA', 'MSFT', 'NVDA',
                                    'TM', 'AAPL', 'WMT', 'CSCO', 'V', 'PG'],
                      data = data_24_01.pct_change(),
                      start_time = '24/11/2021',
                      end_time = '01/12/2021')

Our portfolio rate of return during last week

growth_during_week(data_stock = data_24_01)

Other stok prices

create_others_price(all_watch_list = watch_list,
                    portfolio_list = ['FB', 'TSLA', 'MSFT', 'NVDA',
                                      'TM', 'AAPL', 'WMT', 'CSCO', 'V', 'PG'],
                    start_time = '2021-10-10',
                    end_time = '2021-12-02')

Sell all FB stock

# overall FB value
FB_value = count_stock['FB'] * data_24_01['FB'][-1]
del count_stock['FB']

Sell all WMT stock

# overall WMT value
WMT_value = count_stock['WMT'] * data_24_01['WMT'][-1]
del count_stock['WMT']

Sell all V stock

# overall V value
V_value = count_stock['V'] * data_24_01['V'][-1]
del count_stock['V']

Buy JNJ stock with $4000

Buy AAPL stock with $7593.88

count_stock['JNJ'] = (4000 / price_stock_in_date('JNJ','2021-12-02'))
count_stock['AAPL'] += ((FB_value + WMT_value + V_value - 4000) / price_stock_in_date('AAPL','2021-12-02'))

count_stock

{'TSLA': 20.427123033135686,
 'MSFT': 15.820552884763933,
 'NVDA': 153.74489711577996,
 'TM': 0.6916057658915029,
 'AAPL': 120.1888602893951,
 'CSCO': 108.03024668878925,
 'PG': 40.21758765336398,
 'JNJ': 25.303643431602005}

data_02_12=portfolio_df(start_time='2021-12-02',
                         end_time='2021-12-02',
                         stock_list=['TSLA','MSFT', 'NVDA', 'TM', 'AAPL','CSCO','PG','JNJ'],
                         count_stock=count_stock)

data_02_12

	TSLA	MSFT	NVDA	TM	AAPL	CSCO	PG	JNJ	Portfolio
Date
2021-12-01	1095.0	330.079987	314.350006	178.289993	164.770004	55.029999	146.399994	158.080002	111679.041293

Portfolio description in 01/12/2021

description_portfolio(watch_list = ['TSLA','MSFT', 'NVDA', 'TM',
                                    'AAPL','CSCO','PG','JNJ'],
                      data_portfolio = data_02_12,
                      count_stock = count_stock)

Price of TSLA in 2021-12-01 is ----> 1095.0
Weight of TSLA in our portfolio is ----> 0.2003
Overall value of TSLA in our portfolio is ----> 22367.7
Count of TSLA stock is ----> 20.427

##################################################

Price of MSFT in 2021-12-01 is ----> 330.08
Weight of MSFT in our portfolio is ----> 0.0468
Overall value of MSFT in our portfolio is ----> 5222.048
Count of MSFT stock is ----> 15.821

##################################################

Price of NVDA in 2021-12-01 is ----> 314.35
Weight of NVDA in our portfolio is ----> 0.4328
Overall value of NVDA in our portfolio is ----> 48329.709
Count of NVDA stock is ----> 153.745

##################################################

Price of TM in 2021-12-01 is ----> 178.29
Weight of TM in our portfolio is ----> 0.0011
Overall value of TM in our portfolio is ----> 123.306
Count of TM stock is ----> 0.692

##################################################

Price of AAPL in 2021-12-01 is ----> 164.77
Weight of AAPL in our portfolio is ----> 0.1773
Overall value of AAPL in our portfolio is ----> 19803.519
Count of AAPL stock is ----> 120.189

##################################################

Price of CSCO in 2021-12-01 is ----> 55.03
Weight of CSCO in our portfolio is ----> 0.0532
Overall value of CSCO in our portfolio is ----> 5944.904
Count of CSCO stock is ----> 108.03

##################################################

Price of PG in 2021-12-01 is ----> 146.4
Weight of PG in our portfolio is ----> 0.0527
Overall value of PG in our portfolio is ----> 5887.855
Count of PG stock is ----> 40.218

##################################################

Price of JNJ in 2021-12-01 is ----> 158.08
Weight of JNJ in our portfolio is ----> 0.0358
Overall value of JNJ in our portfolio is ----> 4000.0
Count of JNJ stock is ----> 25.304

##################################################

Rebalance 08/12/2021

data_01_08 = portfolio_df(start_time='2021-12-02',
                          end_time='2021-12-09',
                          stock_list=['TSLA','MSFT', 'NVDA', 'TM',
                                      'AAPL','CSCO','PG','JNJ'],
                          count_stock=count_stock)


data_01_08

	TSLA	MSFT	NVDA	TM	AAPL	CSCO	PG	JNJ	Portfolio
Date
2021-12-01	1095.000000	330.079987	314.350006	178.289993	164.770004	55.029999	146.399994	158.080002	111679.041293
2021-12-02	1084.599976	329.489990	321.260010	183.160004	163.759995	56.150002	147.259995	157.089996	112532.149230
2021-12-03	1014.969971	323.010010	306.929993	182.460007	161.839996	56.230000	149.880005	159.380005	108744.836425
2021-12-06	1009.010010	326.190002	300.369995	184.589996	165.320007	56.959999	152.149994	162.940002	108344.802751
2021-12-07	1051.750000	334.920013	324.269989	187.440002	171.179993	58.080002	151.750000	163.360001	113852.284402
2021-12-08	1068.959961	334.970001	318.260010	182.479996	175.080002	57.020000	152.429993	164.339996	113683.561789

portfolio_values=portfolio_values.append(data_01_08['Portfolio'][1:])

Portfolio values during all time

create_line_chart(index_chart = portfolio_values.index,
                  values_chart = portfolio_values.values,
                  end_time = '08/12/2021')

Portdolio description before rebalance in 08/12/2021

description_portfolio(watch_list = ['TSLA','MSFT', 'NVDA', 'TM',
                                    'AAPL','CSCO','PG','JNJ'],
                      data_portfolio = data_01_08,
                      count_stock = count_stock)

Price of TSLA in 2021-12-08 is ----> 1068.96
Weight of TSLA in our portfolio is ----> 0.1921
Overall value of TSLA in our portfolio is ----> 21835.777
Count of TSLA stock is ----> 20.427

##################################################

Price of MSFT in 2021-12-08 is ----> 334.97
Weight of MSFT in our portfolio is ----> 0.0466
Overall value of MSFT in our portfolio is ----> 5299.411
Count of MSFT stock is ----> 15.821

##################################################

Price of NVDA in 2021-12-08 is ----> 318.26
Weight of NVDA in our portfolio is ----> 0.4304
Overall value of NVDA in our portfolio is ----> 48930.852
Count of NVDA stock is ----> 153.745

##################################################

Price of TM in 2021-12-08 is ----> 182.48
Weight of TM in our portfolio is ----> 0.0011
Overall value of TM in our portfolio is ----> 126.204
Count of TM stock is ----> 0.692

##################################################

Price of AAPL in 2021-12-08 is ----> 175.08
Weight of AAPL in our portfolio is ----> 0.1851
Overall value of AAPL in our portfolio is ----> 21042.666
Count of AAPL stock is ----> 120.189

##################################################

Price of CSCO in 2021-12-08 is ----> 57.02
Weight of CSCO in our portfolio is ----> 0.0542
Overall value of CSCO in our portfolio is ----> 6159.885
Count of CSCO stock is ----> 108.03

##################################################

Price of PG in 2021-12-08 is ----> 152.43
Weight of PG in our portfolio is ----> 0.0539
Overall value of PG in our portfolio is ----> 6130.367
Count of PG stock is ----> 40.218

##################################################

Price of JNJ in 2021-12-08 is ----> 164.34
Weight of JNJ in our portfolio is ----> 0.0366
Overall value of JNJ in our portfolio is ----> 4158.401
Count of JNJ stock is ----> 25.304

##################################################

Our stok proces

create_all_line_chart(watch_list = ['TSLA','MSFT', 'NVDA', 'TM',
                                    'AAPL','CSCO','PG','JNJ'],
                      data = data_01_08,
                      start_time = '01/12/2021',
                      end_time = '08/12/2021')

Our stock rate of return

create_all_line_chart(watch_list = ['TSLA','MSFT', 'NVDA', 'TM',
                                    'AAPL','CSCO','PG','JNJ'],
                      data = data_01_08.pct_change(),
                      start_time = '01/12/2021',
                      end_time = '08/12/2021')

Our portfolio rate of return during last week

growth_during_week(data_stock = data_01_08)

Other stok prices

create_others_price(all_watch_list = watch_list,
                    portfolio_list = ['TSLA','MSFT', 'NVDA', 'TM',
                                      'AAPL','CSCO','PG','JNJ'],
                    start_time = '2021-10-20',
                    end_time = '2021-12-09')

Our portfolio stocks prices during last 2 month

create_line_chart_port(watch_list_port = ['TSLA','MSFT', 'NVDA', 'TM',
                                     'AAPL','CSCO','PG','JNJ'],
                      start_time = '2021-10-10',
                      end_time = '2021-12-09')

SELL stocks

# overall TM value
TM_value = count_stock['TM'] * data_01_08['TM'][-1]
del count_stock['TM']

# overall CSCO value
CSCO_value = count_stock['CSCO'] * data_01_08['CSCO'][-1]
del count_stock['CSCO']

# overall MSFT value
MSFT_value = count_stock['MSFT'] * data_01_08['MSFT'][-1]
del count_stock['MSFT']

Overall sell

TM_value + CSCO_value + MSFT_value

11585.49955197368

BUY stocks

# Buy FB stock with $4000
count_stock['FB'] = (4000 / price_stock_in_date('FB','2021-12-09'))

# Buy MA stock with $4000
count_stock['MA'] = (4000 / price_stock_in_date('MA','2021-12-09'))

# Buy V stock with $3585.5
count_stock['V'] = ((TM_value + CSCO_value + MSFT_value - 8000) / price_stock_in_date('V','2021-12-09'))

Sell $15000 NVDA

Buy $15000 AAPL

count_stock['NVDA'] -= (15000 / price_stock_in_date('NVDA','2021-12-09'))
count_stock['AAPL'] += (15000 / price_stock_in_date('AAPL','2021-12-09'))

data_09_12=portfolio_df(start_time='2021-12-09',
                         end_time='2021-12-09',
                         stock_list=['TSLA', 'NVDA', 'AAPL','PG','JNJ', 'FB', 'MA', 'V'],
                         count_stock=count_stock)

data_09_12

	TSLA	NVDA	AAPL	PG	JNJ	FB	MA	V	Portfolio
Date
2021-12-08	1068.959961	318.26001	175.080002	152.429993	164.339996	330.559998	342.339996	208.990005	113683.561789

Portfolio description in 08/12/2021

description_portfolio(watch_list = ['TSLA', 'NVDA', 'AAPL','PG',
                                    'JNJ', 'FB', 'MA', 'V'],
                      data_portfolio = data_09_12,
                      count_stock = count_stock)

Price of TSLA in 2021-12-08 is ----> 1068.96
Weight of TSLA in our portfolio is ----> 0.1921
Overall value of TSLA in our portfolio is ----> 21835.777
Count of TSLA stock is ----> 20.427

##################################################

Price of NVDA in 2021-12-08 is ----> 318.26
Weight of NVDA in our portfolio is ----> 0.2985
Overall value of NVDA in our portfolio is ----> 33930.852
Count of NVDA stock is ----> 106.614

##################################################

Price of AAPL in 2021-12-08 is ----> 175.08
Weight of AAPL in our portfolio is ----> 0.317
Overall value of AAPL in our portfolio is ----> 36042.666
Count of AAPL stock is ----> 205.864

##################################################

Price of PG in 2021-12-08 is ----> 152.43
Weight of PG in our portfolio is ----> 0.0539
Overall value of PG in our portfolio is ----> 6130.367
Count of PG stock is ----> 40.218

##################################################

Price of JNJ in 2021-12-08 is ----> 164.34
Weight of JNJ in our portfolio is ----> 0.0366
Overall value of JNJ in our portfolio is ----> 4158.401
Count of JNJ stock is ----> 25.304

##################################################

Price of FB in 2021-12-08 is ----> 330.56
Weight of FB in our portfolio is ----> 0.0352
Overall value of FB in our portfolio is ----> 4000.0
Count of FB stock is ----> 12.101

##################################################

Price of MA in 2021-12-08 is ----> 342.34
Weight of MA in our portfolio is ----> 0.0352
Overall value of MA in our portfolio is ----> 4000.0
Count of MA stock is ----> 11.684

##################################################

Price of V in 2021-12-08 is ----> 208.99
Weight of V in our portfolio is ----> 0.0315
Overall value of V in our portfolio is ----> 3585.5
Count of V stock is ----> 17.156

##################################################

Rebalance 15/12/2021

data_08_15 = portfolio_df(start_time='2021-12-09',
                          end_time='2021-12-16',
                          stock_list=['TSLA', 'NVDA', 'AAPL','PG',
                                      'JNJ', 'FB', 'MA', 'V'],
                          count_stock=count_stock)

data_08_15

	TSLA	NVDA	AAPL	PG	JNJ	FB	MA	V	Portfolio
Date
2021-12-08	1068.959961	318.260010	175.080002	152.429993	164.339996	330.559998	342.339996	208.990005	113683.561789
2021-12-09	1003.799988	304.899994	174.559998	153.320007	165.899994	329.820007	344.519989	211.389999	110954.081380
2021-12-10	1017.030029	301.980011	179.449997	155.460007	165.490005	329.750000	349.920013	213.399994	112092.121686
2021-12-13	966.409973	281.609985	175.740005	157.669998	168.449997	334.489990	345.309998	211.020004	108249.063306
2021-12-14	958.510010	283.369995	174.330002	158.250000	170.289993	333.739990	340.549988	209.979996	107972.411153
2021-12-15	975.989990	304.589996	179.300003	158.860001	171.139999	341.660004	346.350006	212.309998	111864.584180

portfolio_values=portfolio_values.append(data_08_15['Portfolio'][1:])

Portfolio values during all time

create_line_chart(index_chart = portfolio_values.index,
                  values_chart = portfolio_values.values,
                  end_time = '15/12/2021')

Portdolio description before rebalance in 15/12/2021

description_portfolio(watch_list = ['TSLA', 'NVDA', 'AAPL','PG',
                                    'JNJ', 'FB', 'MA', 'V'],
                      data_portfolio = data_08_15,
                      count_stock = count_stock)

Price of TSLA in 2021-12-15 is ----> 975.99
Weight of TSLA in our portfolio is ----> 0.1782
Overall value of TSLA in our portfolio is ----> 19936.668
Count of TSLA stock is ----> 20.427

##################################################

Price of NVDA in 2021-12-15 is ----> 304.59
Weight of NVDA in our portfolio is ----> 0.2903
Overall value of NVDA in our portfolio is ----> 32473.443
Count of NVDA stock is ----> 106.614

##################################################

Price of AAPL in 2021-12-15 is ----> 179.3
Weight of AAPL in our portfolio is ----> 0.33
Overall value of AAPL in our portfolio is ----> 36911.412
Count of AAPL stock is ----> 205.864

##################################################

Price of PG in 2021-12-15 is ----> 158.86
Weight of PG in our portfolio is ----> 0.0571
Overall value of PG in our portfolio is ----> 6388.966
Count of PG stock is ----> 40.218

##################################################

Price of JNJ in 2021-12-15 is ----> 171.14
Weight of JNJ in our portfolio is ----> 0.0387
Overall value of JNJ in our portfolio is ----> 4330.466
Count of JNJ stock is ----> 25.304

##################################################

Price of FB in 2021-12-15 is ----> 341.66
Weight of FB in our portfolio is ----> 0.037
Overall value of FB in our portfolio is ----> 4134.318
Count of FB stock is ----> 12.101

##################################################

Price of MA in 2021-12-15 is ----> 346.35
Weight of MA in our portfolio is ----> 0.0362
Overall value of MA in our portfolio is ----> 4046.854
Count of MA stock is ----> 11.684

##################################################

Price of V in 2021-12-15 is ----> 212.31
Weight of V in our portfolio is ----> 0.0326
Overall value of V in our portfolio is ----> 3642.458
Count of V stock is ----> 17.156

##################################################

Our stok prices

create_all_line_chart(watch_list = ['TSLA', 'NVDA', 'AAPL','PG',
                                    'JNJ', 'FB', 'MA', 'V'],
                      data = data_08_15,
                      start_time = '08/12/2021',
                      end_time = '15/12/2021')

Our stock rate of return

create_all_line_chart(watch_list = ['TSLA', 'NVDA', 'AAPL','PG',
                                    'JNJ', 'FB', 'MA', 'V'],
                      data = data_08_15.pct_change(),
                      start_time = '08/12/2021',
                      end_time = '15/12/2021')

Our portfolio rate of return during last week

growth_during_week(data_stock = data_08_15)

Other stok prices

create_others_price(all_watch_list = watch_list,
                    portfolio_list = ['TSLA', 'NVDA', 'AAPL','PG',
                                      'JNJ', 'FB', 'MA', 'V'],
                    start_time = '2021-11-02',
                    end_time = '2021-12-16')

Our portfolio stocks prices during last 2 month

create_line_chart_port(watch_list_port = ['TSLA', 'NVDA', 'AAPL','PG',
                                          'JNJ', 'FB', 'MA', 'V'],
                      start_time = '2021-10-15',
                      end_time = '2021-12-16')

SELL stocks

# overall TSLA value
TSLA_value = count_stock['TSLA'] * data_08_15['TSLA'][-1]
del count_stock['TSLA']

TSLA_value

19936.667609626475

BUY stocks

# Buy JNJ stock with $4000
count_stock['JNJ'] += (4000 / price_stock_in_date('JNJ','2021-12-16'))

# Buy FB stock with $4000
count_stock['FB'] += (4000 / price_stock_in_date('FB','2021-12-16'))

# Buy MA stock with $4000
count_stock['MA'] += (4000 / price_stock_in_date('MA','2021-12-16'))

# Buy V stock with $4000
count_stock['V'] += (4000 / price_stock_in_date('V','2021-12-16'))

# Buy PG stock with $3585.5
count_stock['PG'] += ((TSLA_value - 16000) / price_stock_in_date('PG','2021-12-16'))

data_16_12=portfolio_df(start_time='2021-12-16',
                         end_time='2021-12-16',
                         stock_list=['NVDA', 'AAPL','PG',
                                     'JNJ', 'FB', 'MA', 'V'],
                         count_stock=count_stock)

data_16_12

	NVDA	AAPL	PG	JNJ	FB	MA	V	Portfolio
Date
2021-12-15	304.589996	179.300003	158.860001	171.139999	341.660004	346.350006	212.309998	111864.58418

Portfolio description in 15/12/2021 after rebalance

description_portfolio(watch_list = ['NVDA', 'AAPL','PG',
                                    'JNJ', 'FB', 'MA', 'V'],
                      data_portfolio = data_16_12,
                      count_stock = count_stock)

Price of NVDA in 2021-12-15 is ----> 304.59
Weight of NVDA in our portfolio is ----> 0.2903
Overall value of NVDA in our portfolio is ----> 32473.443
Count of NVDA stock is ----> 106.614

##################################################

Price of AAPL in 2021-12-15 is ----> 179.3
Weight of AAPL in our portfolio is ----> 0.33
Overall value of AAPL in our portfolio is ----> 36911.412
Count of AAPL stock is ----> 205.864

##################################################

Price of PG in 2021-12-15 is ----> 158.86
Weight of PG in our portfolio is ----> 0.0923
Overall value of PG in our portfolio is ----> 10325.634
Count of PG stock is ----> 64.998

##################################################

Price of JNJ in 2021-12-15 is ----> 171.14
Weight of JNJ in our portfolio is ----> 0.0745
Overall value of JNJ in our portfolio is ----> 8330.466
Count of JNJ stock is ----> 48.676

##################################################

Price of FB in 2021-12-15 is ----> 341.66
Weight of FB in our portfolio is ----> 0.0727
Overall value of FB in our portfolio is ----> 8134.318
Count of FB stock is ----> 23.808

##################################################

Price of MA in 2021-12-15 is ----> 346.35
Weight of MA in our portfolio is ----> 0.0719
Overall value of MA in our portfolio is ----> 8046.854
Count of MA stock is ----> 23.233

##################################################

Price of V in 2021-12-15 is ----> 212.31
Weight of V in our portfolio is ----> 0.0683
Overall value of V in our portfolio is ----> 7642.458
Count of V stock is ----> 35.997

##################################################

Rebalance 22/12/2021

data_15_22 = portfolio_df(start_time='2021-12-16',
                          end_time='2021-12-23',
                          stock_list=['NVDA', 'AAPL','PG',
                                      'JNJ', 'FB', 'MA', 'V'],
                          count_stock=count_stock)

data_15_22

	NVDA	AAPL	PG	JNJ	FB	MA	V	Portfolio
Date
2021-12-15	304.589996	179.300003	158.860001	171.139999	341.660004	346.350006	212.309998	111864.584180
2021-12-16	283.869995	172.259995	161.110001	173.009995	334.899994	352.920013	214.369995	108509.388578
2021-12-17	278.010010	171.139999	157.460007	168.229996	333.790009	349.309998	211.880005	106984.220639
2021-12-20	277.190002	169.750000	157.860001	167.750000	325.450012	337.019989	208.740005	106016.152818
2021-12-21	290.750000	172.990005	157.759995	167.210007	334.200012	350.739990	215.380005	108862.149499
2021-12-22	294.000000	175.639999	159.320007	167.929993	330.450012	357.480011	217.960007	110050.810183

portfolio_values=portfolio_values.append(data_15_22['Portfolio'][1:])

Portfolio values during all time

create_line_chart(index_chart = portfolio_values.index,
                  values_chart = portfolio_values.values,
                  end_time = '22/12/2021')

Portfolio description before rebalance in 22/12/2021

description_portfolio(watch_list = ['NVDA', 'AAPL','PG',
                                    'JNJ', 'FB', 'MA', 'V'],
                      data_portfolio = data_15_22,
                      count_stock = count_stock)

Price of NVDA in 2021-12-22 is ----> 294.0
Weight of NVDA in our portfolio is ----> 0.2848
Overall value of NVDA in our portfolio is ----> 31344.405
Count of NVDA stock is ----> 106.614

##################################################

Price of AAPL in 2021-12-22 is ----> 175.64
Weight of AAPL in our portfolio is ----> 0.3286
Overall value of AAPL in our portfolio is ----> 36157.949
Count of AAPL stock is ----> 205.864

##################################################

Price of PG in 2021-12-22 is ----> 159.32
Weight of PG in our portfolio is ----> 0.0941
Overall value of PG in our portfolio is ----> 10355.533
Count of PG stock is ----> 64.998

##################################################

Price of JNJ in 2021-12-22 is ----> 167.93
Weight of JNJ in our portfolio is ----> 0.0743
Overall value of JNJ in our portfolio is ----> 8174.214
Count of JNJ stock is ----> 48.676

##################################################

Price of FB in 2021-12-22 is ----> 330.45
Weight of FB in our portfolio is ----> 0.0715
Overall value of FB in our portfolio is ----> 7867.428
Count of FB stock is ----> 23.808

##################################################

Price of MA in 2021-12-22 is ----> 357.48
Weight of MA in our portfolio is ----> 0.0755
Overall value of MA in our portfolio is ----> 8305.441
Count of MA stock is ----> 23.233

##################################################

Price of V in 2021-12-22 is ----> 217.96
Weight of V in our portfolio is ----> 0.0713
Overall value of V in our portfolio is ----> 7845.84
Count of V stock is ----> 35.997

##################################################

Our stok prices

create_all_line_chart(watch_list = ['NVDA', 'AAPL','PG',
                                    'JNJ', 'FB', 'MA', 'V'],
                      data = data_15_22,
                      start_time = '15/12/2021',
                      end_time = '22/12/2021')

Our stock rate of return

create_all_line_chart(watch_list = ['NVDA', 'AAPL','PG',
                                    'JNJ', 'FB', 'MA', 'V'],
                      data = data_15_22.pct_change(),
                      start_time = '15/12/2021',
                      end_time = '22/12/2021')

Our portfolio rate of return during last week

growth_during_week(data_stock = data_15_22)

Other stok prices

create_others_price(all_watch_list = watch_list,
                    portfolio_list = ['NVDA', 'AAPL','PG',
                                      'JNJ', 'FB', 'MA', 'V'],
                    start_time = '2021-11-02',
                    end_time = '2021-12-23')

Our portfolio stocks prices during last 2 month

create_line_chart_port(watch_list_port = ['NVDA', 'AAPL','PG',
                                          'JNJ', 'FB', 'MA', 'V'],
                      start_time = '2021-10-23',
                      end_time = '2021-12-23')

buy TSLA, CSCO, IBM, sell FB, JNJ, MA, V

SELL stocks

# overall FB value
FB_value = count_stock['FB'] * data_15_22['FB'][-1]
del count_stock['FB']

# overall JNJ value
JNJ_value = count_stock['JNJ'] * data_15_22['JNJ'][-1]
del count_stock['JNJ']

# overall MA value
MA_value = count_stock['MA'] * data_15_22['MA'][-1]
del count_stock['MA']

# overall V value
V_value = count_stock['V'] * data_15_22['V'][-1]
del count_stock['V']

Partly sell

# Sell NVDA stock with $10000
count_stock['NVDA'] -= (10000 / price_stock_in_date('NVDA','2021-12-23'))

# Sell AAPL stock with $10000
count_stock['AAPL'] -= (10000 / price_stock_in_date('AAPL','2021-12-23'))

sell_22 = FB_value + JNJ_value + MA_value + V_value + 2 * 10000

BUY stocks

# Buy TSLA stock with $20000
count_stock['TSLA'] = (20000 / price_stock_in_date('TSLA','2021-12-23'))

# Buy CSCO stock with $16000
count_stock['CSCO'] = (16000 / price_stock_in_date('CSCO','2021-12-23'))

# Buy IBM stock with $16193
count_stock['IBM'] = ((sell_22 - 20000 - 16000) / price_stock_in_date('IBM','2021-12-23'))

data_22_12=portfolio_df(start_time='2021-12-23',
                         end_time='2021-12-23',
                         stock_list=['NVDA', 'AAPL','PG',
                                     'TSLA', 'CSCO', 'IBM'],
                         count_stock=count_stock)

data_22_12

	NVDA	AAPL	PG	TSLA	CSCO	IBM	Portfolio
Date
2021-12-22	294.0	175.639999	159.320007	1008.869995	61.529999	129.75	110050.810183

Portfolio description in 22/12/2021 after rebalance

description_portfolio(watch_list = ['NVDA', 'AAPL','PG',
                                    'TSLA', 'CSCO', 'IBM'],
                      data_portfolio = data_22_12,
                      count_stock = count_stock)

Price of NVDA in 2021-12-22 is ----> 294.0
Weight of NVDA in our portfolio is ----> 0.194
Overall value of NVDA in our portfolio is ----> 21344.405
Count of NVDA stock is ----> 72.6

##################################################

Price of AAPL in 2021-12-22 is ----> 175.64
Weight of AAPL in our portfolio is ----> 0.2377
Overall value of AAPL in our portfolio is ----> 26157.949
Count of AAPL stock is ----> 148.929

##################################################

Price of PG in 2021-12-22 is ----> 159.32
Weight of PG in our portfolio is ----> 0.0941
Overall value of PG in our portfolio is ----> 10355.533
Count of PG stock is ----> 64.998

##################################################

Price of TSLA in 2021-12-22 is ----> 1008.87
Weight of TSLA in our portfolio is ----> 0.1817
Overall value of TSLA in our portfolio is ----> 20000.0
Count of TSLA stock is ----> 19.824

##################################################

Price of CSCO in 2021-12-22 is ----> 61.53
Weight of CSCO in our portfolio is ----> 0.1454
Overall value of CSCO in our portfolio is ----> 16000.0
Count of CSCO stock is ----> 260.036

##################################################

Price of IBM in 2021-12-22 is ----> 129.75
Weight of IBM in our portfolio is ----> 0.1471
Overall value of IBM in our portfolio is ----> 16192.923
Count of IBM stock is ----> 124.801

##################################################

Final result in 29/12/2021

data_22_29 = portfolio_df(start_time='2021-12-23',
                          end_time='2021-12-30',
                          stock_list=['NVDA', 'AAPL','PG',
                                      'TSLA', 'CSCO', 'IBM'],
                          count_stock=count_stock)

data_22_29

	NVDA	AAPL	PG	TSLA	CSCO	IBM	Portfolio
Date
2021-12-22	294.000000	175.639999	159.320007	1008.869995	61.529999	129.750000	110050.810183
2021-12-23	296.399994	176.279999	160.100006	1067.000000	62.279999	130.630005	111828.293848
2021-12-27	309.450012	180.330002	161.970001	1093.939941	63.419998	131.619995	114454.490254
2021-12-28	303.220001	179.289993	162.839996	1088.469971	63.529999	132.630005	113950.068482
2021-12-29	300.010010	179.380005	164.190002	1086.189941	63.959999	133.350006	113974.649174

portfolio_values=portfolio_values.append(data_22_29['Portfolio'][1:])

Portfolio values during all time

create_line_chart(index_chart = portfolio_values.index,
                  values_chart = portfolio_values.values,
                  end_time = '29/12/2021')

Portfolio description in 29/12/2021

description_portfolio(watch_list = ['NVDA', 'AAPL','PG',
                                    'TSLA', 'CSCO', 'IBM'],
                      data_portfolio = data_22_29,
                      count_stock = count_stock)

Price of NVDA in 2021-12-29 is ----> 300.01
Weight of NVDA in our portfolio is ----> 0.1911
Overall value of NVDA in our portfolio is ----> 21780.732
Count of NVDA stock is ----> 72.6

##################################################

Price of AAPL in 2021-12-29 is ----> 179.38
Weight of AAPL in our portfolio is ----> 0.2344
Overall value of AAPL in our portfolio is ----> 26714.946
Count of AAPL stock is ----> 148.929

##################################################

Price of PG in 2021-12-29 is ----> 164.19
Weight of PG in our portfolio is ----> 0.0936
Overall value of PG in our portfolio is ----> 10672.075
Count of PG stock is ----> 64.998

##################################################

Price of TSLA in 2021-12-29 is ----> 1086.19
Weight of TSLA in our portfolio is ----> 0.1889
Overall value of TSLA in our portfolio is ----> 21532.803
Count of TSLA stock is ----> 19.824

##################################################

Price of CSCO in 2021-12-29 is ----> 63.96
Weight of CSCO in our portfolio is ----> 0.1459
Overall value of CSCO in our portfolio is ----> 16631.887
Count of CSCO stock is ----> 260.036

##################################################

Price of IBM in 2021-12-29 is ----> 133.35
Weight of IBM in our portfolio is ----> 0.146
Overall value of IBM in our portfolio is ----> 16642.207
Count of IBM stock is ----> 124.801

##################################################

DSB 21/22